WO2023083088A1 - Procédé et appareil d'optimisation adaptative de poids 5g, dispositif informatique et support d'enregistrement informatique - Google Patents

Procédé et appareil d'optimisation adaptative de poids 5g, dispositif informatique et support d'enregistrement informatique Download PDF

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WO2023083088A1
WO2023083088A1 PCT/CN2022/129509 CN2022129509W WO2023083088A1 WO 2023083088 A1 WO2023083088 A1 WO 2023083088A1 CN 2022129509 W CN2022129509 W CN 2022129509W WO 2023083088 A1 WO2023083088 A1 WO 2023083088A1
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grid
coverage
path loss
weight combination
cluster
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PCT/CN2022/129509
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Chinese (zh)
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朱文涛
高峰
李文智
王西点
吴远
张晨曦
刘斐
靳侃侃
徐金鹏
高明皓
王时檬
赵永红
邱禹
郝佳佳
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中国移动通信集团设计院有限公司
中国移动通信集团有限公司
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Publication of WO2023083088A1 publication Critical patent/WO2023083088A1/fr

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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/18—Network planning tools
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/10—Scheduling measurement reports ; Arrangements for measurement reports
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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  • the present invention relates to the field of communication technology, in particular to a 5G weight self-adaptive optimization method, device, computing equipment and computer storage medium.
  • the depth coverage optimization of wireless signals in high-rise vertical scenes has always been a difficult problem in network optimization.
  • the existing 5th Generation Mobile Communication Technology (5G) weight optimization schemes for vertical scenes mainly include: , 3D)
  • the high-precision map is directly layered according to the floor height, and the simulation optimization is directly carried out in combination with the 5G beam scene of the equipment manufacturer.
  • simulate 5G cell distribution through 4G Minimization Drive Test (MDT) data, and the 4G building cell information can be obtained from the MDT data of 4G cells.
  • MDT Minimization Drive Test
  • MDT data contains latitude and longitude information, which can be combined with 3D high-precision electronic maps to geographically present the coverage of 4G wireless signals in buildings, automatically identify 4G building cell information, and sort out 4G/5G cell co-site coverage Then, according to the distance between the 5G cell and the building it covers, the height of the building, and the basic antenna feed information of the 5G cell, the 5G vertical wave width suitable for building coverage is calculated through an algorithm.
  • embodiments of the present invention are proposed to provide a 5G weight adaptive optimization method, device, computing device, and computer storage medium that overcome the above problems or at least partially solve the above problems.
  • a 5G weight adaptive optimization method including:
  • a 5G weight adaptive optimization device including:
  • the grid mapping part is configured to establish a three-dimensional grid, and map the MR sampling points of the 5G measurement report to each grid in the three-dimensional grid;
  • the grid identification part is configured to identify whether the corresponding grid is a business grid or a deep weak coverage grid based on the sampling point data in each grid;
  • the service path loss determining part is configured to determine a service distribution center grid cluster based on the service grid, and determine a first path loss between the center grid and the cell grid of the service distribution center grid cluster;
  • the service grid evaluation part is configured to evaluate the coverage of the center grid of the service distribution center grid cluster based on the first path loss, determine a weight combination that improves the coverage, and obtain the initial weight value combination;
  • the depth weak coverage grid evaluation part is configured to evaluate the coverage of the depth weak coverage grid based on the second path loss between the depth weak coverage grid and the cell grid in the initial weight combination, and determine The weight combination with improved coverage is obtained to obtain the final weight combination.
  • a computing device including: a processor, a memory, a communication interface, and a communication bus, and the processor, the memory, and the communication interface complete the mutual communication via the communication bus. communication between
  • the memory is used to store at least one executable instruction, and the executable instruction causes the processor to execute the operation of the above-mentioned 5G weight adaptive optimization method.
  • a computer storage medium is provided, and at least one executable instruction is stored in the storage medium, and the executable instruction causes the processor to perform the above-mentioned 5G weight adaptive optimization method. operate.
  • the business grid and the depth weak coverage grid can be automatically identified, and the coverage effect of the weight combination on the business grid and the depth weak coverage grid can be evaluated at the same time, so as to select the best The optimal weight combination improves the accuracy of 5G wireless signal coverage evaluation.
  • FIG. 1 shows a flowchart of a 5G weight adaptive optimization method provided by Embodiment 1 of the present invention
  • FIG. 2 shows a schematic diagram of a vertical plane in the 5G weight adaptive optimization method provided by Embodiment 1 of the present invention
  • FIG. 3 shows a schematic diagram of a horizontal plane in the 5G weight adaptive optimization method provided by Embodiment 1 of the present invention
  • FIG. 4 shows a schematic diagram of antenna gains in the 5G weight adaptive optimization method provided by Embodiment 1 of the present invention
  • FIG. 5 shows another flow chart of the 5G weight adaptive optimization method provided by Embodiment 1 of the present invention.
  • FIG. 6 shows a schematic structural diagram of a 5G weight adaptive optimization device provided by Embodiment 2 of the present invention
  • Fig. 7 shows a schematic structural diagram of a computing device provided by an embodiment of the present invention.
  • FIG. 1 shows a flowchart of a 5G weight adaptive optimization method provided by Embodiment 1 of the present invention. As shown in Figure 1, the method includes the following steps:
  • Step S110 establishing a three-dimensional grid, and mapping 5G measurement report (Measurement Report, MR) sampling points to each grid in the three-dimensional grid.
  • mapping 5G measurement report Measurement Report, MR
  • WGS84 World Geodetic System 1984
  • WGS84 can be used to establish a three-dimensional grid of 10 meters * 10 meters * 10 meters, and three parameters of grid center point longitude X, latitude Y and height H are used to represent each grid specific location.
  • the WGS84 is a coordinate system established for use by the Global Positioning System (GPS).
  • GPS Global Positioning System
  • the association between the 5G MR sampling points and the three-dimensional grid needs to be established, that is, the 5G MR sampling points are mapped to each grid in the three-dimensional grid.
  • the latitude, longitude and altitude can be obtained by locating the 5G MR sampling points in combination with the base station industrial parameter data.
  • the 5G MR sampling point data carries VDOA (vertical direction angle relative to the antenna, similar to the downtilt angle from the sampling point to the antenna), HDOA (horizontal direction angle relative to the antenna, similar to the azimuth from the sampling point to the normal direction of the antenna) Angle), TA (distance from the antenna), combined with the antenna's latitude and longitude, height, and azimuth angle, after calculation, the latitude and longitude (see Figure 3) and height (see Figure 2) of each 5G MR sampling point can be obtained. After that, each 5G MR sampling point is mapped according to its longitude, latitude, height and three-dimensional grid. Among them, Table 1 is an example of 5G MR sampling points:
  • the synchronization signal ssrsrp (synchronization signal reference signal received power) is the average power of the synchronization signal on each carrier.
  • the calculation method of the height of the sampling point is shown in Figure 2. Assuming VDOA is ⁇ , the antenna height is H, and the distance from the sampling point to the antenna is TA, then the height of the sampling point h2 is calculated by formula (1) and formula (2).
  • the three-dimensional (x, y, z) coordinates of each 5G MR sampling point can be obtained by the following latitude-longitude coordinate conversion formula (6) and formula (7):
  • Step S120 based on the sampling point data in each grid, identify whether the corresponding grid is a service grid or a deep weak coverage grid.
  • the depth weak coverage grid is defined as: the number of sampling points in the grid is greater than a preset threshold (for example, 80) and the average Reference Signal Received Power (Reference Signal Receiving Power, RSRP) is less than the first preset threshold (for example, -105dBm ); the deep weak coverage grid can also be defined as RSRP less than a second preset threshold (for example, -110dBm) and the proportion of sampling points is greater than a preset percentage (for example, 20%).
  • a preset threshold for example, 80
  • RSRP Reference Signal Receiving Power
  • the total number of sampling points in each grid, the average RSRP, and the proportion of sampling points with deep weak coverage are calculated through 5G MR sampling points.
  • the following judgment is made during the calculation, if the number of sampling points in the grid i is greater than 80, directly use the following formula (8) to find its average RSRP:
  • RSRP j is the RSRP value of the jth 5G MR sampling point in the grid i
  • J is the total number of 5G MR sampling points in the grid i.
  • the number of sampling points in grid i is less than or equal to 80, sort the sampling points in grid i in the order of RSRP from small to large, select all sampling points less than -110dBm, and calculate the sampling points less than -110dBm
  • the proportion of all sampling points in grid i if the proportion is greater than 20%, it means that the grid is a depth weak coverage grid, and only these 20% sampling points are calculated by formula (8), and it is obtained And put it into the data set D weak ; on the contrary, if the RSRP value is less than -110dBm sampling points account for less than 20% of all sampling points in grid i, then calculate through formula (8), and get And put it into the data set D business .
  • Step S130 Determine the service distribution center grid cluster based on the service grid, and determine the first path loss between the center grid of the service distribution center grid cluster and the cell grid.
  • the 5G MR sampling points have been associated with the three-dimensional grid, and according to the quality and quantity of the 5G MR sampling points in each three-dimensional grid, the service grid and the grid with insufficient depth coverage (that is, the depth weak coverage grid), and averaged the RSRP values of the MR sampling points in each grid, and took the average as the RSRP value of the grid.
  • the business distribution center grid cluster is determined based on the business grid.
  • the value of the business grid is sorted according to the number of sampling points, and the number of sampling points is used as the criterion for judging the value.
  • the value judgment of each business grid is expressed as Among them, h is the community, i is the business grid, starting from the business grid with the highest value, it is added in sequence according to the order to form a three-dimensional graph, and the business value of all the business grids in the three-dimensional graph is calculated until the business value in the three-dimensional graph accounts for When the proportion of the total business value of the service area exceeds 80%, the convergence ends, and a high-value business distribution three-dimensional figure is formed, that is, the business distribution central grid cluster.
  • the center of this three-dimensional figure is the central grid of the service distribution central grid cluster.
  • the path loss of the scene is calculated based on the actual RSRP value of the grid in the vertical scene. After that, assuming that the default path loss remains unchanged, starting from the location of the cell, determine the center grid of the central grid cluster of service distribution. Calculate the path loss between the cell grid and the center grid, and use it as the default path loss for beam optimization in the service distribution center.
  • the determination of the service distribution center grid cluster based on the service grid may be implemented in the following manner. According to the value of each business grid, add the corresponding business grid into the business grid set in descending order, until the sum of the values of all business grids in the business grid set is greater than the first preset threshold, the current The business grid collection of is used as the business distribution center grid cluster.
  • Step1 For the set D business composed of business grids, calculate the value ⁇ of each grid in the set D business , and sort all the grids according to the value ⁇ from large to small, and the serial number is n business ;
  • Step2 Add the grid with the smallest serial number in the set D business to the set C business , and delete it from the set D business at the same time;
  • Step3 Calculate the sum ⁇ of the ⁇ values of all objects in the set C business , and judge whether ⁇ is greater than 0.8.
  • Step4 Repeat steps Step2 to Step3, stop when ⁇ >0.8, and output the business area set C business , which is the business distribution center grid cluster.
  • Step5. Extract the center point of the service area, that is, the center grid of the service distribution center grid cluster.
  • the center point of the service area is shown in formula (9).
  • Step6 Record the RSRP value of the center point of the business area (from formula (8)) according to Calculate the first path loss.
  • Step 6 for example, according to the Friesian transmission formula in the antenna theory, as shown in formula (10).
  • P r is the received power
  • P t is the transmitted power
  • G t is the gain of the transmitter
  • G r is the gain of the receiver
  • ⁇ is the wavelength of the transmitted electromagnetic wave
  • R is the distance between the transmitter and the receiver.
  • the path loss that is, the path loss L
  • the path loss L can be simplified as shown in formula (12).
  • RSRP is the measured RSRP of the grid
  • P is the transmission power
  • G is the antenna gain (which can be read from the three-dimensional gain pattern of the antenna).
  • Step S140 based on the first path loss, evaluate the coverage of the central grid of the service distribution central grid cluster, determine a weight combination that improves the coverage, and obtain an initial weight combination.
  • the combination of weights includes at least one weight of electron orientation angle, electron downtilt angle, horizontal wave width and vertical wave width.
  • multiple weight combinations can be set based on the first path loss, and the multiple weight combinations are respectively used to evaluate the coverage of the central grid of the service distribution central grid cluster, and to filter out the service distribution central grid
  • the weight combination with improved coverage of the central grid of the grid cluster is obtained to obtain the initial weight combination.
  • the initial weight combination includes multiple sets of weight combinations.
  • Step S150 based on the second path loss between the deep weak coverage grid and the cell grid in the initial weight combination, evaluate the coverage of the deep weak coverage grid, determine the weight combination that improves the coverage, and obtain The final weight combination.
  • the second path loss between the depth weak coverage grid and the cell grid is determined first, and the coverage of the depth weak coverage grid is determined based on the weight combination in the initial weight combination and the corresponding second path loss.
  • the final weight combination includes at least one set of weight combinations.
  • this embodiment is applicable to the situation where there are few depth weak coverage grids and their distribution is relatively scattered. Although the coverage evaluation for all depth weak coverage grids will consume certain computing resources and time resources, the initial weight There is a more refined evaluation of the weak coverage boosting effect of value combinations.
  • the final weight combination If it is not empty, you can directly output a set of weight combinations as the final output.
  • the final weight combination of the output is generally used
  • the first set of weight combinations in As the final output, the central grid of the service distribution central grid cluster has coverage improvement (RSRP new -RSRP>3dbm) and the deep weak coverage grid has the best improvement (corresponding to the largest ) weight combination.
  • one solution uses 3D high-precision maps combined with device manufacturers’ 5G beam scenarios to directly perform simulation optimization, only relying on simulation and building information, without real user information, it is impossible to establish The feedback mechanism, therefore, can only be used as an initialization scheme for the wireless network, i.e. only one adjustment is made, reducing the accuracy of the assessment of the 5G wireless signal coverage situation.
  • the 5G vertical wave width suitable for building coverage is calculated according to the building height and the horizontal position information of the 4G MDT data. There is no 5G user information, and only the user horizontal position information in the 4G MDT data is used to locate the 5G network. Covering cells is equivalent to using two-dimensional data to optimize the coverage of three-dimensional space. Therefore, vertical dimension information in vertical scenarios cannot be identified, which reduces the accuracy of 5G wireless signal coverage evaluation.
  • the present invention embodies a method for self-adaptive optimization of 5G weight parameters based on vertical scenarios, using the vertical dimension information in the cell-level 5G MR data (that is, each MR data can correspond to a corresponding cell) to establish a three-dimensional grid, through Carry out cluster analysis on the problem grid, locate the user distribution in the vertical scene and locate the coverage problem in the vertical scene, so as to output an adaptive 5G weight scheme.
  • the weight combination that can simultaneously improve the coverage of the business area and the weak coverage area is selected, and the optimization of the 5G vertical coverage scene is realized, thereby improving
  • the 5G resident ratio, user perception and other indicators have been improved, and the accuracy of the 5G weight optimization scheme has been improved.
  • the embodiment of the present invention can automatically identify the service grid and the depth weak coverage grid while generating the 5G MR three-dimensional grid, and use the current antenna transmission power and the three-dimensional gain pattern of the antenna to simultaneously evaluate the combination of weights in the service grid.
  • the coverage effect on the grid and depth weak coverage grid is used to select the optimal weight combination, and the calculation results can cover all default weight combinations, which improves the accuracy of 5G wireless signal coverage evaluation.
  • step S140 may include:
  • Step S1401. Calculate the combination of theoretical weights according to the central point of the service grid.
  • the theoretical weight combination may be a theoretical weight quadruple composed of electron direction angle, electron downtilt angle, horizontal wave width and vertical wave width.
  • Electronic downtilt calculation first calculate the coordinates (x h , y h , z h ) and business center point of cell h The height difference between is shown in formula (15).
  • tilt is the mechanical downtilt angle of cell h.
  • Calculation of horizontal wave width in the business distribution center grid cluster set C business , find out all the business centers Points in the same plane, that is, to find all points in the plane
  • the set of business points in for collections For each point in , use the formula (14) to calculate the angle between each point and the cell location coordinates (x h , y h , z h ) and the y-axis (that is, the direction of true north), and find the maximum The angle angle max and the minimum angle angle min .
  • the total orientation angle angle total between the cell and the center of the service grid calculated according to formula (14) can be selected from the values of 2*(angle max -angle total ) and 2*(angle total -angle min )
  • the maximum is the ideal horizontal wave width hbw ideal of the output.
  • Step S1402 according to the default 5G weight table of each equipment manufacturer, map the theoretical weight combination to the actual supportable weight combination of each equipment manufacturer.
  • a default weight combination close to the ideal weight quadruple is selected from the default weight table, and the default weight table is traversed, and the step size of the electronic downtilt (default The minimum is 1°) and the step size of the electron orientation angle (default The minimum is 1°), find out all the weight combinations that can satisfy the coverage of the ideal weight quadruple (eazimuth ideal , etilt ideal , hbw ideal , vbw ideal ) (that is, the weight combination can contain the ideal weight quadruple Coverage), which can ensure that the obtained new weight combination has a good coverage of the existing business distribution area.
  • the ideal weight quadruple eazimuth ideal , etilt ideal , hbw ideal , vbw ideal
  • the second way is to traverse the default weight table as a whole.
  • the step size of the electronic downtilt (default The minimum is 1°) and the step size of the electron orientation angle (default The minimum is 1°)
  • find out all weight combinations that cover more than 95% of the business grid set C business in this way, as many optional weight combinations as possible can be obtained under the premise of ensuring the coverage of existing services to a certain extent.
  • the second method When the weight combination calculated by the first method is empty, the second method is automatically triggered; if the weight set calculated by the second method is still empty, the step size Reduce the ratio of the number of grids covered by the business, and perform the calculation again until it is reduced to 75%, and stop without further reduction.
  • Step S1403 Evaluate the coverage of the center grid of the service distribution center grid cluster based on the antenna gain, antenna transmit power, and first path loss corresponding to the supported weight combination.
  • the above step S1403 may be implemented in the following manner. Based on the antenna gain, antenna transmit power and first path loss corresponding to the supportable weight combination, determine the reference signal received power RSRP of the center grid of each service distribution center grid cluster; based on the center grid of each service distribution center grid cluster The RSRP of the grid determines the coverage improvement of the central grid of each business distribution central grid cluster; based on the coverage improvement of the central grid of each business distribution central grid cluster, the weight combination that improves the coverage is determined.
  • the RSRP new at this time is RSRP new -RSRP>3dbm compared with the value before adjustment, that is, when the coverage improvement is greater than the preset threshold, it means that the weight combination covers the center grid of the current business distribution center grid cluster If it can be improved effectively, the obtained weight combination W i is used as the initial weight combination. After the traversal is completed, the initial weight combination ⁇ W ⁇ is output.
  • step S150 may include:
  • Step S1501 when the number of depth weak coverage grids is greater than the second preset threshold and the distribution is concentrated, perform clustering processing on the depth weak coverage grids to obtain depth weak coverage grid clusters.
  • the depth can be analyzed by a three-dimensional density-based clustering method (Density-Based Spatial Clustering of Applications with Noise, DBSCAN) with noise Weakly covered grids are clustered to improve the efficiency of subsequent calculations.
  • a three-dimensional density-based clustering method Density-Based Spatial Clustering of Applications with Noise, DBSCAN
  • the implementation algorithm is as follows:
  • Input a data set D weak containing n weak depth and weak coverage grids, the longitude, latitude and height information of each grid center point has been converted into three-dimensional coordinate system coordinates (xi , y , zi ) ;
  • Output a set of deep weakly covered raster clusters C weak based on density clustering
  • Step. Mark all depth weak coverage grids as unvisited
  • Step2 When there is a grid marked as unvisited; execute 1-4;
  • N be the set in the ⁇ neighborhood of p, for each point p′ in N;
  • Step3 Output collection ⁇ C weak ⁇ .
  • the above-mentioned algorithm for locating the center grid of the business distribution center grid cluster can also be used to locate the center grid of each depth weak coverage sufficient grid cluster Combined with formula (8) calculated for subsequent basis Prepare for the second path loss between the center grid and the cell grid of the deep weak coverage grid cluster.
  • Step S1502. Determine the second path loss between the center grid and the cell grid of the deep weak coverage grid cluster.
  • the calculation method of the second path loss in this step is the same as the calculation method of the first path loss.
  • Step S1503 Evaluate the coverage of the center grid of the deep weak coverage grid cluster based on the antenna gain, antenna transmission power and second path loss corresponding to the initial weight combination.
  • step S1503 may be implemented in the following manner. Based on the antenna gain, antenna transmission power and second path loss corresponding to the initial weight combination, determine the RSRP of the center grid of each service distribution center grid cluster; determine the coverage based on the RSRP of the center grid of the M depth weak coverage grid clusters Lifting degree: Evaluate the coverage of the center grid of the deep weak coverage grid cluster based on the coverage lifting degree.
  • k represents the kth set of initial weights in the initial weight combination ⁇ W ⁇ (there are K sets of weights in total), Indicates the mean value of RSRP before the cluster center grid optimization, That is, the evaluation of the overall improvement of the set of weights to the weak coverage grid set ⁇ C weak ⁇ .
  • the initial weight combination ⁇ W ⁇ can also be called a candidate weight set, and all K sets of weights in the candidate weight set ⁇ W ⁇ are evaluated by formula (17) and formula (18). discard all For the weight combination whose value is less than 0, for the remaining weight combination in the initial weight combination ⁇ W ⁇ , according to Sort the values from large to small to get the final weight combination
  • the final weight combination If it is not empty, you can directly output a set of weight combinations as the final output.
  • the final weight combination of the output is generally used
  • the first set of weight combinations in As the final output, the central grid of the service distribution central grid cluster has coverage improvement (RSRP new -RSRP>3dbm) and the deep weak coverage grid has the best improvement (corresponding to the largest ) weight combination.
  • This embodiment is suitable for evaluating the coverage of the central grid of each depth weak coverage grid cluster, that is, it is suitable for the situation where there are many depth weak coverage grids and their distribution is relatively concentrated, and only for each depth weak coverage grid cluster Evaluate the coverage of the central grid, which can effectively reduce computing consumption and improve computing efficiency.
  • FIG. 5 shows another flowchart of the 5G weight adaptive optimization method provided by Embodiment 1 of the present invention.
  • the 5G weight adaptive optimization method includes S51-S65.
  • the business area and the deep weak coverage area of the three-dimensional coverage area of the real vertical scene are located.
  • S54 and S62 are respectively performed based on the volumetric grid mapping of the MR data.
  • the RSRP data in the MR data is combined with the propagation model to evaluate the path loss of the link from the cell to the grid, so as to prepare for the evaluation of the subsequent weight scheme.
  • the propagation model is used to calculate the link loss in the service area and the deep weak coverage area, and evaluate the coverage effect of each set of candidate weight schemes, so as to find the optimal solution among them, and improve the accuracy of the coverage effect and effectiveness.
  • the embodiment of the present invention adopts the three-dimensional DBSCAN density clustering algorithm to cluster the weakly covered grids, which reduces the amount of data to be processed by subsequent algorithms, and is suitable for scenes with many deep weakly covered grids, while ensuring the calculation accuracy of the algorithm , which improves the efficiency of the algorithm and realizes the analysis of big data.
  • FIG. 6 shows a schematic structural diagram of a 5G weight adaptive optimization device provided by Embodiment 2 of the present invention.
  • the device includes: a grid mapping part 31, a grid identification part 32, a service path loss determination part 33, a service grid evaluation part 34 and a deep weak coverage grid evaluation part 35; wherein,
  • the grid mapping part 31 is configured to establish a three-dimensional grid, and map the MR sampling points of the 5G measurement report into each grid in the three-dimensional grid;
  • the grid identification part 32 is configured to identify whether the corresponding grid is a service grid or a deep weak coverage grid based on the sampling point data in each grid;
  • the service path loss determining part 33 is configured to determine a service distribution center grid cluster based on the service grid, and determine a first path loss between the center grid and the cell grid of the service distribution center grid cluster;
  • the service grid evaluation part 34 is configured to evaluate the coverage of the center grid of the service distribution center grid cluster based on the first path loss, determine a weight combination that improves the coverage, and obtain the initial weight value combination;
  • the depth weak coverage grid evaluation part 35 is configured to evaluate the coverage of the depth weak coverage grid based on the second path loss between the depth weak coverage grid and the cell grid in the initial weight combination, and determine The weight combination with improved coverage is obtained to obtain the final weight combination.
  • the service path loss determining part 33 is further configured to: according to the value of each service grid, add the corresponding service grid into the service grid set in descending order, until the service grid If the sum of the values of all business grids in the grid set is greater than the first preset threshold, the current business grid set is used as the business distribution center grid cluster.
  • the service grid evaluation part 34 is further configured to: calculate a combination of theoretical weights according to the center point of the service grid; combine the theoretical weights according to the default 5G weight table of each equipment manufacturer Mapping to the actual supportable weight combination of each equipment manufacturer; based on the antenna gain, antenna transmission power and first path loss corresponding to the supportable weight combination, the central grid of the service distribution central grid cluster coverage is evaluated.
  • the depth weak coverage grid evaluation part 35 is further configured to: when the number of depth weak coverage grids is greater than a second preset threshold and the distribution is relatively concentrated, perform clustering on the depth weak coverage grids processing to obtain the depth weak coverage grid cluster; determine the second path loss between the central grid of the depth weak coverage grid cluster and the cell grid; combine the corresponding antenna gain, antenna transmission power and the second path loss based on the initial weight value combination The second path loss evaluates the coverage of the central grid of the deep weak coverage grid cluster.
  • the service grid evaluation part 34 is further configured to: determine the value of each service distribution center grid cluster based on the antenna gain, antenna transmit power, and first path loss corresponding to the supportable weight combination
  • the reference signal received power RSRP of the central grid based on the RSRP of the central grids of the central grid clusters of each service distribution, determine the coverage improvement range of the central grids of the central grid clusters of each service distribution;
  • the coverage improvement of the center grid determines the combination of weights that improves the coverage.
  • the deep weak coverage grid evaluation part 35 is further configured to: determine the value of each service distribution center grid cluster based on the antenna gain, antenna transmission power, and second path loss corresponding to the initial weight combination The RSRP of the central grid; determine the coverage improvement based on the RSRP of the central grids of the M deep weak coverage grid clusters; evaluate the coverage of the central grids of the deep weak coverage grid clusters based on the coverage improvement .
  • the combination of weights includes at least one weight of electron direction angle, electron downtilt angle, horizontal wave width and vertical wave width.
  • the 5G weight adaptive optimization device described in the embodiment of the present invention is used to implement the 5G weight adaptive optimization method described in the above embodiment, and its working principle is similar to the technical effect, so it will not be repeated here.
  • An embodiment of the present invention provides a non-volatile computer storage medium, the computer storage medium stores at least one executable instruction, and the computer executable instruction can execute the 5G weight adaptive optimization method in any of the above method embodiments .
  • FIG. 7 shows a schematic structural diagram of a computing device provided by an embodiment of the present invention, and the specific embodiment of the present invention does not limit the specific implementation of the computing device.
  • the computing device may include: a processor (processor), a communication interface (Communications Interface), a memory (memory), and a communication bus.
  • a processor processor
  • a communication interface Communication Interface
  • a memory memory
  • a communication bus a communication bus
  • the processor, the communication interface, and the memory complete the mutual communication through the communication bus.
  • the communication interface is used to communicate with network elements of other devices such as clients or other servers.
  • the processor is configured to execute the program, specifically, it can execute the relevant steps in the above-mentioned embodiments of the 5G weight adaptive optimization method for computing equipment and the cell azimuth prediction method.
  • the program may include program code, and the program code includes computer operation instructions.
  • the processor may be a central processing unit CPU, or an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present invention.
  • the one or more processors included in the computing device may be of the same type, such as one or more CPUs, or may be different types of processors, such as one or more CPUs and one or more ASICs.
  • the memory may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.
  • the program may specifically be used to enable the processor to execute the 5G weight adaptive optimization method in any of the above method embodiments.
  • each step in the program refer to the description of the corresponding steps and units in the above-mentioned embodiment of the 5G weight adaptive optimization method, and details are not repeated here.
  • Those skilled in the art can clearly understand that for the convenience and brevity of the description, the specific working process of the above-described devices and parts can refer to the corresponding process description in the foregoing method embodiments, and details are not repeated here.
  • parts of the devices in the embodiment can be adaptively changed and arranged in one or more devices different from the embodiment.
  • Parts or units or components in the embodiments may be combined into one part or unit or component, and further they may be divided into a plurality of sub-parts or sub-units or sub-assemblies.
  • All features disclosed in this specification including accompanying claims, abstract and drawings) and any method or method so disclosed may be used in any combination, except that at least some of such features and/or processes or units are mutually exclusive. All processes or units of equipment are combined.
  • Each feature disclosed in this specification may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
  • the various component embodiments of the present invention may be implemented in hardware, or in software running on one or more processors, or in a combination thereof.
  • a microprocessor or a digital signal processor (DSP) may be used in practice to implement some or all functions of some or all components according to the embodiments of the present invention.
  • Embodiments of the present invention can also be implemented as a device or apparatus program (eg, computer program and computer program product) for performing a part or all of the methods described herein.
  • Such a program implementing an embodiment of the present invention may be stored on a computer-readable medium, or may be in the form of one or more signals.
  • Such a signal may be downloaded from an Internet site, or provided on a carrier signal, or provided in any other form.
  • the invention discloses a 5G weight adaptive optimization method, device, computing equipment and computer storage medium.
  • the 5G weight adaptive optimization method includes: mapping the 5G measurement report MR sampling points to each grid in the three-dimensional grid grid; determine the first path loss between the center grid of the service distribution center grid cluster and the cell grid; evaluate the coverage of the center grid of the service distribution center grid cluster based on the first path loss, and determine the The weight combination with improved coverage is obtained to obtain the initial weight combination; based on the second path loss between the depth weak coverage grid and the cell grid in the initial weight combination, the coverage of the depth weak coverage grid is evaluated, Determine the combination of weights that improves the coverage to obtain the final combination of weights.
  • the embodiment of the present invention can automatically identify the service grid and the depth weak coverage grid, and can simultaneously evaluate the coverage effect of the weight combination on the service grid and the depth weak coverage grid, so as to select the optimal weight combination, Improved the accuracy of 5G wireless signal coverage evaluation.

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  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Mobile Radio Communication Systems (AREA)
  • Radar Systems Or Details Thereof (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

Sont divulgués un procédé et un appareil d'optimisation adaptative de poids 5G, un dispositif informatique et un support d'enregistrement informatique. Le procédé comprend : la mise en correspondance d'un point d'échantillonnage de rapport de mesure (MR) 5G dans chaque grille dans une grille tridimensionnelle ; la détermination d'une première perte de trajet entre une grille centrale d'une grappe de grilles centrales de distribution de service et une grille de cellules ; l'évaluation d'une condition de couverture de la grille centrale de la grappe de grilles centrales de distribution de service sur la base de la première perte de trajet et la détermination d'une combinaison de poids améliorant ladite condition de couverture pour obtenir une combinaison de poids initiale ; l'évaluation d'une condition de couverture d'une grille de couverture faible profonde sur la base d'une seconde perte de trajet entre la grille de couverture faible profonde dans la combinaison de poids initiale et la grille de cellules, et la détermination d'une combinaison de poids améliorant ladite condition de couverture pour obtenir une combinaison de poids finale. La présente invention peut identifier automatiquement une grille de service et une grille de couverture faible profonde, et évaluer l'effet de couverture d'une combinaison de poids sur la grille de service et la grille de couverture faible profonde, de telle sorte qu'une combinaison de poids optimale est sélectionnée et la précision d'évaluation est améliorée.
PCT/CN2022/129509 2021-11-11 2022-11-03 Procédé et appareil d'optimisation adaptative de poids 5g, dispositif informatique et support d'enregistrement informatique WO2023083088A1 (fr)

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Citations (5)

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CN108271171A (zh) * 2016-12-31 2018-07-10 中国移动通信集团辽宁有限公司 建筑物网络状况统计方法及装置
CN111343648A (zh) * 2018-12-19 2020-06-26 中国移动通信集团辽宁有限公司 一种区域分析方法、装置及设备
WO2020135450A1 (fr) * 2018-12-29 2020-07-02 中兴通讯股份有限公司 Procédé et dispositif de génération de base de données d'informations d'empreinte digitale radiofréquence et de positionnement de grille
CN113015192A (zh) * 2021-04-07 2021-06-22 中国移动通信集团陕西有限公司 天线权值确定方法、装置、设备及存储介质
CN113131974A (zh) * 2019-12-30 2021-07-16 中国移动通信集团四川有限公司 一种基于3dmimo的天线权值自动寻优的方法及装置

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108271171A (zh) * 2016-12-31 2018-07-10 中国移动通信集团辽宁有限公司 建筑物网络状况统计方法及装置
CN111343648A (zh) * 2018-12-19 2020-06-26 中国移动通信集团辽宁有限公司 一种区域分析方法、装置及设备
WO2020135450A1 (fr) * 2018-12-29 2020-07-02 中兴通讯股份有限公司 Procédé et dispositif de génération de base de données d'informations d'empreinte digitale radiofréquence et de positionnement de grille
CN113131974A (zh) * 2019-12-30 2021-07-16 中国移动通信集团四川有限公司 一种基于3dmimo的天线权值自动寻优的方法及装置
CN113015192A (zh) * 2021-04-07 2021-06-22 中国移动通信集团陕西有限公司 天线权值确定方法、装置、设备及存储介质

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